Linear programming (LP) algorithms using a simplex method have been known since the 1940s to solve linear programming problems. Some say George Dantzig likely proposed the first linear programming algorithm. To date, linear programming remains an efficient and reliable method for solving such problems. The speed and efficiency with which linear programs run is dependent on the underlying platform used. For example, the GUROBI™ simplex solver can be on average multiple times faster than some open-source simplex solvers. The difference comes down to some main factors: sparse linear algebra, handling of numerical errors, and effective heuristic strategies.
Regarding sparse linear algebra, the constraint matrices that arise in linear programming can sometimes be extremely sparse; sparse matrices/vectors contain few non-zero entries. Sparse matrices may need to be factored, systems of sparse linear equations may need to be solved using the resulting factor matrices, the factor matrices may need to be modified, etc. Regarding the careful handling of numerical errors, whenever systems of linear equations are solved in finite-precision arithmetic, slight numerical errors in the results are inevitable. Efficient LP algorithms include strategies for managing such errors. Finally, regarding heuristic strategies for making selections during the course of the solution process, the difference can mean a more efficient optimization scheme.
Furthermore, algorithms are known in the art for calculating a premium amount for an insurance policy. For example, some insurance companies use factors such as a territory factor (e.g., a zip code/location of an asset), age factor (e.g., number of months in the life of a person or asset), vehicle factor, insurance score factor, claim history factor, and other factors known to a person having ordinary skill in the art, in order to calculate a premium amount for a particular insurance policy. However, such prior art algorithms leave substantial room for improvement.
In addition, in traditional insurance systems, each of the aforementioned insurance factors is assessed, the system obtains/assigns values for each factor, and then engages in various calculations to determine an insurance rate/premium to assign to the insurance policy. When numerous factors are being assessed, the requisite calculations can require substantial coordination with one or more systems and processor-intensive, time-consuming calculations.
Furthermore, in the insurance industry, determining how to price an insurance policy is a complex process and is sometimes referred to as an insurance rating system or an insurance rating function. A large amount of information is inputted into an insurance rating system, then it is processed/analyzed, and a premium (e.g., price) for an insurance policy is outputted. Given the large amount of data involved, insurance rating systems are typically memory intensive and/or computation intensive. Particularly, the table scheme poses a big hurdle when implementing changes to the rating plan, or even implementing a new rating plan; every single step may need to be programmed and tested individually. It can be time consuming and expensive to update an established rating system. This has led to long lead times and high costs in going to market, thus existing insurance rating systems are inefficient and often slow to update.
Thus, there is much room for improvement in the way insurance rating systems are designed and operate/function, including systems and methods for more efficiently updating the technological systems that calculate insurance rates/premiums.